In a clinical environment, observing respiratory activity (breathing frequency) is highly relevant. Pulse and respiration are one of the most important basic vital signs to assess the health status of a patient. In intensive care unit (ICU) settings, pulse and respiration are routinely measured via ECG electrodes from the electrocardiogram, and the measured thorax-impedance changes during breathing activity, respectively.
Doppler radar sensors have been identified as a promising technology for contactless measurements of respiration and cardiac activity. A large extent of research activities has been focused on Radar Systems at frequencies above 60 GHz. Today, low-power low-cost Doppler radar sensors are commercially available, mainly for activity detection in homes in the frequency range of <25 GHz. These sensors might be an interesting low-cost solution for remote vital signs monitoring, but they require more efforts in development for intelligent signal analysis, since state-of-the-art signal processing approaches are hardly applicable for these sensors. The main reason is that the wavelengths are large (approx. 10 . . . 120 mm) compared to the motion amplitudes of the thorax caused by respiration and the beating heart.
In Doppler radar sensors, generally, a sender/receiver unit continuously emits electromagnetic waves towards a target. The electromagnetic waves are reflected at the target and travel back to the sender/receiver. Two mixers/receivers are employed in order to evaluate the received signal. The first mixer downconverts the signal received directly at the antenna; the second mixer downconverts the antenna signal after it was phase-shifted by 90 degrees.
A radar sensor has the advantages that no direct skin contact is required. The speed and the direction of movement as well as a change of direction is coded in the measured signals, but especially for operating frequencies <25 GHz, state-of-the-art detection schemes are hardly applicable. Therefore, correct and reliable interpretation of these signals is challenging. However, reliable and comfortable detection of respiration activity in clinical settings is an unmet need today.